On‑device AI curates wallpapers from your preferences
Vanderwaals uses on-device machine learning to understand your aesthetic preferences and automatically surfaces wallpapers you'll love—no endless scrolling required. 🤖 Neural network (100% offline) 🔒 Zero tracking, zero analytics 📚 3,000+ curated wallpapers from GitHub + Bing ⚡ Auto-change on unlock/hourly/daily 🎨 Material 3 with dynamic theming 🔓 Fully open source (AGPL-3.0) Two modes: Start fresh and let AI learn, or upload one favorite wallpaper for instant similar matches.
I’m Avinash, a solo indie developer from India, and I’m excited to finally share Vanderwaals with you.
🌱 Why I Built This:
I love changing wallpapers—but I kept running into the same problems:
Endless scrolling to find something that actually matched my taste
Wallpaper apps that quietly track everything and push data to the cloud
“AI” recommendations that never truly understood my aesthetic
I wanted something personal, private, and intelligent—so I built it myself.
✨ What Vanderwaals Does:
Vanderwaals is an offline, on-device AI wallpaper app that learns your visual taste over time.
It uses on-device machine learning to extract 576-dimensional visual embeddings from wallpapers and adapts based on what you like or dislike—no internet required.
You can use it in two simple ways:
1. Auto Mode Start from scratch. Like or dislike wallpapers, and the AI gradually tunes itself to your aesthetic.
2. Personalize Mode Upload one favorite wallpaper → instantly get 100+ visually similar results.
🔐 Privacy Is the Core Feature:
This was non-negotiable for me.
Runs 100% offline
No cloud ML APIs
No analytics
No tracking
No data collection
Fully open source (audit everything yourself)
Your aesthetic preferences are deeply personal—they should never leave your device.
🛠️ Built With
Kotlin + Jetpack Compose (Material 3)
TensorFlow Lite (on-device inference)
Room Database
WorkManager for automation
Dagger Hilt
Under the hood:
Cosine similarity for visual matching
LAB color space for perceptual accuracy
Exponential Moving Average (EMA) for adaptive learning
🖼️ Wallpaper Library:
8,000+ curated wallpapers
GitHub aesthetic collections
Bing’s daily photography archive
Weekly auto-sync for fresh content
⏳ 6 Months, One Developer
This project was built during late nights and weekends. Along the way, I learned a lot about mobile ML optimization, Android’s WorkManager quirks, and how to make AI feel natural instead of robotic.
Special shout-out to Anthony La’s Paperize project—it inspired the wallpaper infrastructure.
Hey Product Hunters 👋
I’m Avinash, a solo indie developer from India, and I’m excited to finally share Vanderwaals with you.
🌱 Why I Built This:
I love changing wallpapers—but I kept running into the same problems:
Endless scrolling to find something that actually matched my taste
Wallpaper apps that quietly track everything and push data to the cloud
“AI” recommendations that never truly understood my aesthetic
I wanted something personal, private, and intelligent—so I built it myself.
✨ What Vanderwaals Does:
Vanderwaals is an offline, on-device AI wallpaper app that learns your visual taste over time.
It uses on-device machine learning to extract 576-dimensional visual embeddings from wallpapers and adapts based on what you like or dislike—no internet required.
You can use it in two simple ways:
1. Auto Mode
Start from scratch. Like or dislike wallpapers, and the AI gradually tunes itself to your aesthetic.
2. Personalize Mode
Upload one favorite wallpaper → instantly get 100+ visually similar results.
🔐 Privacy Is the Core Feature:
This was non-negotiable for me.
Runs 100% offline
No cloud ML APIs
No analytics
No tracking
No data collection
Fully open source (audit everything yourself)
Your aesthetic preferences are deeply personal—they should never leave your device.
🛠️ Built With
Kotlin + Jetpack Compose (Material 3)
TensorFlow Lite (on-device inference)
Room Database
WorkManager for automation
Dagger Hilt
Under the hood:
Cosine similarity for visual matching
LAB color space for perceptual accuracy
Exponential Moving Average (EMA) for adaptive learning
🖼️ Wallpaper Library:
8,000+ curated wallpapers
GitHub aesthetic collections
Bing’s daily photography archive
Weekly auto-sync for fresh content
⏳ 6 Months, One Developer
This project was built during late nights and weekends. Along the way, I learned a lot about mobile ML optimization, Android’s WorkManager quirks, and how to make AI feel natural instead of robotic.
Special shout-out to Anthony La’s Paperize project—it inspired the wallpaper infrastructure.
🔮 What’s Coming Next:
CLIP embeddings for semantic understanding
Community-contributed collections
Reddit sourcing (r/wallpapers, r/earthporn)
💬 AMA
Happy to answer anything about:
Privacy-first design decisions
Android + ML challenges
Open-source licensing (AGPL-3.0)
Or anything else you’re curious about
Would love your feedback 🙏
GitHub: https://github.com/avinaxhroy/Vanderwaals
Play Store: https://play.google.com/store/apps/details?id=me.avinas.vanderwaals
Made with ❤️ in India 🇮🇳